Aurélie Tournié

Raman identification of glassy silicates used in ceramics, glass and jewellery: a tentative differentiation guide

As an optical method, Raman microspectroscopy offers a great advantage over most other techniques in that it can be performed without any contact with the studied artefact, both at the laboratory using high-resolution, large spectral window instruments, and on site using medium resolution, portable instruments. Six years of experience on various materials has enabled us to propose a tentative guide to identify different types of glassy silicates and to classify them as a function of their composition. In previous papers, different families were recognised empirically using a limited set of samples and the relationship between the peak area ratio (A500/A1000) of the SiO bending (∼500 cm−1) and stretching (i.e. ∼1000 cm−1) envelopes, and the different components of the latter peak were established for porcelain glazes. In this paper, we extend the procedure to a larger set of samples (30 representative samples with known compositions selected from hundreds of spectra). Additional relationships between the origin of the material, the Raman parameters and the glass composition (fluxing oxide content, ionicity ratio, etc.) are discussed. Finally we propose different procedures with different degrees of complexity (from two to multivariate treatment) to identify the glass composition. The method is illustrated with an example, namely, the differentiation between Iznik and Kutahya productions. Copyright

Implementation of a neural network for multispectral luminescence imaging of lake pigment paints.

Luminescence multispectral imaging is a developing and promising technique in the fields of conservation science and cultural heritage studies. In this article, we present a new methodology for recording the spatially resolved luminescence properties of objects. This methodology relies on the development of a lab-made multispectral camera setup optimized to collect low-yield luminescence images. In addition to a classic data preprocessing procedure to reduce noise on the data, we present an innovative method, based on a neural network algorithm, that allows us to obtain radiometrically calibrated luminescence spectra with increased spectral resolution from the low-spectral resolution acquisitions. After preliminary corrections, a neural network is trained using the 15-band multispectral luminescence acquisitions and corresponding spot spectroscopy luminescence data. This neural network is then used to retrieve a megapixel multispectral cube between 460 and 710 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. The resulting data are independent from the detection chain of the imaging system (filter transmittance, spectral sensitivity of the lens and optics, etc.). As a result, the image cube provides radiometrically calibrated emission spectra with increased spectral resolution. For each pixel, we can thus retrieve a spectrum comparable to those obtained with conventional luminescence spectroscopy. We apply this method to a panel of lake pigment paints and discuss the pertinence and perspectives of this new approach.